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Better Late Than Never: Meta-Evaluation of Latency Metrics for Simultaneous Speech-to-Text Translation

Peter Polák, Sara Papi, Luisa Bentivogli, Ondřej Bojar · Sep 22, 2025 · Citations: 0

Automatic Metrics
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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Simultaneous speech-to-text translation systems must balance translation quality with latency. Although quality evaluation is well established, latency measurement remains a challenge. Existing metrics produce inconsistent results, especially in short-form settings with artificial presegmentation. We present the first comprehensive meta-evaluation of latency metrics across language pairs and systems. We uncover a structural bias in current metrics related to segmentation. We introduce YAAL (Yet Another Average Lagging) for a more accurate short-form evaluation and LongYAAL for unsegmented audio. We propose SoftSegmenter, a resegmentation tool based on soft word-level alignment. We show that YAAL and LongYAAL, together with SoftSegmenter, outperform popular latency metrics, enabling more reliable assessments of short- and long-form simultaneous speech translation systems. We implement all artifacts within the OmniSTEval toolkit: https://github.com/pe-trik/OmniSTEval.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

5/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 45%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Simultaneous speech-to-text translation systems must balance translation quality with latency."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Simultaneous speech-to-text translation systems must balance translation quality with latency."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Simultaneous speech-to-text translation systems must balance translation quality with latency."

Benchmarks / Datasets

partial

Omnisteval

Useful for quick benchmark comparison.

"We implement all artifacts within the OmniSTEval toolkit: https://github.com/pe-trik/OmniSTEval."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Simultaneous speech-to-text translation systems must balance translation quality with latency."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: Multilingual

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

Omnisteval

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Simultaneous speech-to-text translation systems must balance translation quality with latency.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Simultaneous speech-to-text translation systems must balance translation quality with latency.
  • Although quality evaluation is well established, latency measurement remains a challenge.
  • Existing metrics produce inconsistent results, especially in short-form settings with artificial presegmentation.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • We present the first comprehensive meta-evaluation of latency metrics across language pairs and systems.
  • We introduce YAAL (Yet Another Average Lagging) for a more accurate short-form evaluation and LongYAAL for unsegmented audio.
  • We propose SoftSegmenter, a resegmentation tool based on soft word-level alignment.

Why It Matters For Eval

  • We present the first comprehensive meta-evaluation of latency metrics across language pairs and systems.
  • We introduce YAAL (Yet Another Average Lagging) for a more accurate short-form evaluation and LongYAAL for unsegmented audio.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Pass: Benchmark or dataset anchors are present

    Detected: Omnisteval

  • Gap: Metric reporting is present

    No metric terms extracted.

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Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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